This invention describes a method for generating processes that facilitate the self-organization of autonomous systems. It can be applied to mechanistic fields as well as to molecular/biological systems. By means of the invention described herein, it is possible for a system in motion to recognize external events in a subjective way through self-observation; to identify the surrounding physical conditions in real time; to reproduce and to optimize the system's own motions; and to enable a redundancy-poor process that leads to self-organization.
Robot systems of the usual static type are mainly based on deterministic path dependent regulating processes. The digital outputs and values that control the robot's position are stored in the memory of a central computer. Many degrees of freedom can be created by a suitable arrangement of coordinating devices. Position detectors can be devices such as tachogenerators, encoders, or barcode rulers scanned by optical sensors that provide path dependent increment pulses. The activation mostly takes place by means of stepper motors.
It is also well-known that additional adaptive regulating processes based on discrete time data are used in path dependent program control units. These data are produced by means of the SHANNON quantization method, utilizing analog-to-digital converters to sample the amplitudes of sensors and transducers. They serve to identify the system's actual value (i.e. its current state). Continued comparison of reference values and actual values are necessary for correction and adjustment of the regulating process. Newly calculated parameters are then stored in the memory. This kind of adaptive regulation is necessary, for example, in order to eliminate a handling robot's deviations from a preprogrammed course that are caused by variable load conditions.
If a vehicle that is robot-controlled in this way were to be placed into an autonomous state, it would generally be impossible to determine its exact position reference (i.e. coordinates) by means of tachogenerators or encoders. For this reason controlling values (or commands) cannot be issued by a computer--or preprogrammed into a computer--in an accurate manner. This is true not only for robot-controlled automobiles, gliding vehicles, hovercraft or aircraft, but also for rail-borne vehicles for which the distance dependent incremental pulses are often inaccurate and therefore not reproducible. This is usually caused by an uneven surface or worn or slipping wheels. Explorer robots, which are used to locate objects or to rescue human beings from highly inaccessible or dangerous locations, must therefore be controlled manually, or with computer supported remote control units. A video communication system is necessary for such cases in order to be able to monitor the motion of the robot. However, in many applications of robotics, this is inadequate. A robot-controlled automobile, for example, should be capable of avoiding dangerous situations in real time, as well as being capable of adapting its speed to suit the environment, without any human intervention. In such cases, it is necessary for the on-board computer to recognize the situation at hand, then calculate automatically the next steps to be carried out. In this way the robot-controlled vehicle ought to have a certain capability for self-organization. This is also true for other robot-controlled systems.
With regards to autonomous robot systems, techniques already exist to scan the surroundings by means of sensors and to analyze the digital sensor data that were acquired using the above-mentioned discrete time quantization method (see FIG. 1); and there already exist statistical calculation methods and algorithms that generate suitable regulating parameters. Statistical methods for handling such regulating systems were described in 1949 by Norbert WIENER. According to the SHANNON theorem, the scanning of the external environment must be done with at least double the frequency of the signal amplitude bandwidth. In this way the information content remains adequate. In order to be able to identify the robot's own motions, very high sampling rates are necessary. This amplitude quantization method currently in widespread use requires the correlation of particular measurement data to particular points in time (Ts) that are predetermined using the program counter. For this reason this should be understood as a deterministic method. However, practical experience has shown that even ultrahigh-speed processors and the highest sampling rates cannot provide sufficient efficiency. The number of redundant data and the amount of computing operations increase drastically when a moving sensor-controlled vehicle meets new obstacles or enters new surroundings at variable speed. Indeed, C. SHANNON's quantization method does not allow recognition of an analogue signal amplitude in real time, especially if there are changing physical conditions or variable motions for which the acquisition of additional information regarding the instantaneous velocity is necessary. This is also true if laser detectors or supersonic sensors are used, for which mainly distance data are acquired and processed. Therefore, although this quantization method is suitable for analyzing the trace of a motion and for representing this motion on a monitor (see Pat. AT 397 869), it is hardly adequate for recognizing the robot's own motion, or for reproducing it in a self-adaptive way.
Some autonomous mobile robot systems operate with CCD sensors and OCR software (i.e. utilising image processing). These deduce contours or objects from color contrast and brightness differentials, which are interpreted by the computer as artificial horizons or orientation marks. Examples of this technology are computer-supported guidance systems and steering systems that allow vehicles to be guided automatically by centre lines, side planks, street edges and so on. CCD sensors--when one observes how they operate--are analog storage devices that function like well-known bucket brigade devices. Tightly packed capacitors placed on a MOS silicon semiconductor chip are charged by the photoelectric effect to a certain electrical potential. Each charge packet represents an individual picture element, termed "pixel"; and the charge of each pixel is a record of how bright that part of the image is. By supplying a pulse frequency, the charges are shifted from pixel to pixel across the CCD, where they appear at the edge output as serial analog video signals. In order to process them in a computer, they must be converted into digital quantities. This requires a large number of redundant data and calculations; this is why digital recording of longer image sequences necessitates an extremely large high speed memory. Recognizing isomorphous sequences in repetitive motions is only possible with large memory and time expenditure, which is why robotic systems based on CCD sensors cannot adequately reproduce their own motion course in a self-adaptive way. With each repetition of the same motion along the same route, all regulating parameters must be calculated by means of picture analysis anew. If environment conditions change through fog, darkness or snowfall, such systems are overburdened. Pat. AT 400 028 describes a system for the adaptive regulation of a motor driven vehicle, in which certain landmarks or signal sources are provided along the vehicle's route in order to serve as bearing markers that allow the robot to keep to a schedule. Positions determined by GPS data can also serve this purpose. When the system passes these sources, the sensor coupled on board computer acquires the elapsed times for all covered route segments by means described in U.S. Pat. No. 4,245,334, which details the manner of time quantization by first and second sensor signals The data acquired in this way serve as a reference base for the computation of regulating parameters that control the drive cycles and brake cycles of the vehicle when a motion repetition happens. The system works with low data redundancy, corrects itself in a self-adaptive manner, and is capable of reproducing an electronic route schedule precisely. It is suitable, for example, for ensuring railway networks keep to schedule. However, in the system detailed in the above-mentioned patent, it is not possible to identify external objects and surroundings.
It is an object of the present invention to provide an extensive method for the creation of autonomous self-organizing robot systems or organisms, which enables them to identify external signals, objects, events, physical conditions or surroundings in real time by observing from their own subjective view. They will be able to recognize their own motion patterns and to reproduce and optimize their behavior in a self-adaptive way. Another object of this invention is the preparation of an autonomous training robot for use in sports, that is capable of identifying, reproducing and optimizing a motion process (e.g. that has been trialed beforehand by an athlet) as well as: determining the ideal track and speed courses automatically; keeping to route schedules; representing its own motion, speeds, lap times, intermediate times and start to finish times on a monitor; and which is capable of acoustic or optical output of the acquired data.